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Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks
- Source :
- PLoS ONE, PLoS ONE, 2021, 16 (11), pp.e0250755. ⟨10.1371/journal.pone.0250755⟩, PLoS ONE, Vol 16, Iss 11 (2021), PLoS ONE, Vol 16, Iss 11, p e0250755 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science, 2021.
-
Abstract
- The analysis of brain-imaging data requires complex and often non-linear transformations to support findings on brain function or pathologies. And yet, recent work has shown that variability in the choices that one makes when analyzing data can lead to quantitatively and qualitatively different results, endangering the trust in conclusions1–3. Even within a given method or analytical technique, numerical instabilities could compromise findings4–7. We instrumented a structural-connectome estimation pipeline with Monte Carlo Arithmetic8, 9, a technique to introduce random noise in floating-point computations, and evaluated the stability of the derived connectomes, their features10, 11, and the impact on a downstream analysis12, 13. The stability of results was found to be highly dependent upon which features of the connectomes were evaluated, and ranged from perfectly stable (i.e. no observed variability across executions) to highly unstable (i.e. the results contained no trustworthy significant information). While the extreme range and variability in results presented here could severely hamper our understanding of brain organization in brain-imaging studies, it also leads to an increase in the reliability of datasets. This paper highlights the potential of leveraging the induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness of their applications in the detection or classification of individual differences. This paper demonstrates that stability evaluations are necessary for understanding error and bias inherent to scientific computing, and that they should be a component of typical analytical workflows.
- Subjects :
- Computer science
Pipeline (computing)
computer.software_genre
Nervous System
Infographics
0302 clinical medicine
Range (statistics)
Medicine and Health Sciences
Instrumentation
Reliability (statistics)
Data Management
0303 health sciences
Brain Mapping
Multidisciplinary
Uncertainty
Software Engineering
Brain
Variance (accounting)
Reproducibility
Physical Sciences
Connectome
Medicine
Engineering and Technology
Data mining
Anatomy
Stability
Graphs
Network Analysis
Network analysis
Research Article
Statistical Distributions
Connectomics
Computer and Information Sciences
Neural Networks
Imaging Techniques
Science
Models, Neurological
Stability (learning theory)
Context (language use)
Neuroimaging
Research and Analysis Methods
Stability (probability)
Computer Software
03 medical and health sciences
Robustness (computer science)
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Humans
030304 developmental biology
Data Visualization
Biology and Life Sciences
Probability Theory
Noise
Neuroanatomy
Nerve Net
Network Neuroscience
computer
030217 neurology & neurosurgery
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....9dad9690d45b34491a1efe6c7e973c13